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Machine-Learning-Enhanced Simulation Could Reduce Energy Costs in Materials Production


Topics: Sustainability, Machine Learning

Organizations: Argonne National Laboratory, 3M

Thanks to a new computational effort being pioneered by the U.S. Department of Energy’s (DOE) Argonne National Laboratory in conjunction with 3M and supported by the DOE’S High Performance Computing for Energy Innovation (HPC4EI) program, researchers are finding new ways to dramatically reduce the amount of energy required for melt blowing the materials needed in N95 masks and other applications.

Currently, the process used to create a nozzle to spin nonwoven materials produces a very high-quality product, but it is quite energy intensive. Approximately 300,000 tons of melt-blown materials are produced annually worldwide, requiring roughly 245 gigawatt-hours per year of energy, approximately the amount generated by a large solar farm. By using Argonne supercomputing resources to pair computational fluid dynamics simulations and machine-learning techniques, the Argonne and 3M collaboration sought to reduce energy consumption by 20% without compromising material quality.

Because the process of making a new nozzle is very expensive, the information gained from the machine-learning model can equip material manufacturers with a way to narrow down to a set of optimal designs. ​”Machine-learning-enhanced simulation is the best way of cheaply getting at the right combination of parameters like temperatures, material composition, and pressures for creating these materials at high quality with less energy,” Blaiszik said.

Read more at AZO Materials